Modeling instructional sensitivity using a longitudinal multilevel differential item functioning approach

Students’ performance in assessments is commonly attributed to more or less effective teaching. This implies that students’ responses are significantly affected by instruction. However, the assumption that outcome measures indeed are instructionally sensitive is scarcely investigated empirically. In...

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Bibliographic Details
Published in:Journal of educational measurement 2014-12, Vol.51 (4), p.381-399
Main Authors: Naumann, Alexander, Hochweber, Jan, Hartig, Johannes
Format: Article
Language:English
Subjects:
Academic achievement
Comparative analysis
Curricula
Curriculum
DESI (Deutsch-Englisch-Schülerleistungen-International)
Educational evaluation
Educational research
Experimental data
Grundschule
Hierarchical Linear Modeling
Instruction
Itemanalyse
Judgment
Leistungsmessung
Longitudinal Studies
Längsschnittuntersuchung
Modell
Multilevel models
Pedagogy
Posttests
Pretests
Pretests Posttests
Qualität
Schülerleistung
Sekundarbereich
Standard deviation
Statistical discrepancies
Teaching
Test Bias
Test Items
Test scores
Testkonstruktion
Unterricht
Validität
Wirkung
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Summary:Students’ performance in assessments is commonly attributed to more or less effective teaching. This implies that students’ responses are significantly affected by instruction. However, the assumption that outcome measures indeed are instructionally sensitive is scarcely investigated empirically. In the present study, we propose a longitudinal multilevel‐differential item functioning (DIF) model to combine two existing yet independent approaches to evaluate items’ instructional sensitivity. The model permits for a more informative judgment of instructional sensitivity, allowing the distinction of global and differential sensitivity. Exemplarily, the model is applied to two empirical data sets, with classical indices (Pretest–Posttest Difference Index and posttest multilevel‐DIF) computed for comparison. Results suggest that the approach works well in the application to empirical data, and may provide important information to test developers. (Orig.).
ISSN:0022-0655
1745-3984
1745-3984
DOI:10.1111/jedm.12051